Effects of Fertilization Rate and Water Availability on Peanut Growth and Yield in Senegal (West Africa)
Bibliographic record
Abstract
The effects of fertilization rate and water availability on peanut growth and yield of two cultivars were investigated in a series of field experiments at Bambey, Nioro and Sinthiou Malem in Senegal. Both rainy and dry season experiments were conducted over two years between 2014 and 2015, for a total of seven experiments. The first set of four experiments were to evaluate fertilizer application rate on peanut production. One experiment was conducted in the dry season 2014 in Nioro with four levels of fertilizer and one experiment in the rainy season 2014 in each of Bambey, Nioro and Sinthiou Malem with six levels of fertilizer in a RCBD with four replications both. The second set of experiments were to evaluate the effect of different water regimes on peanut production. Experiments were conducted in the dry season of 2014 and 2015 in Bambey and in Nioro 2015. The experimental design was a split plot design with four replications and three levels of water, namely, E, S1 and S2. The effects of fertilization rate on peanut in three different sites were not significantly different between fertilizer levels. However, irrigation treatments were significantly different in all sites during the two years. Under water stressed conditions, the seed yield was more affected than the biomass yield. Seed yield decreased by 33% when stress occurred at flowering period and by 50% when stress occurred during seed filling. The most sensitive period for yield declined was observed during the period of maturation followed to the flowering stage. The interaction between irrigation and fertilizer was not signification in both Bambey and Nioro sites of field experiments. Such experiments should be conducted in field based conditions where occur limited soil nutrients to test higher dose of NPK.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".